1. Field of the Invention
This invention relates generally to a procedure for selecting a product by a customer and, more particularly, to methodologies and concomitant circuitry for using latent semantic structure of content ascribed to the products to provide automatic recommendations to the customer.
2. Description of the Background
There are two threads of pertinent subject matter which serve as points of departure for the present invention, namely: (1) work in manipulating personal preferences for recommendations of items; and (2) work in using relevance feedback in information retrieval tasks for items.
The current state of the art with respect to item (1) above is composed of two techniques for providing recommendations. The first is to use a domain expert to handcraft recommendations for a specific item. In this technique, an expert proceeds through a series of items, and notates for each item which additional items should be recommended when a customer chooses the original item. This technique is laborious and is not automatic; for instance, when a new item is introduced, the expert must be consulted again to generate recommendations for the new item. Also, in situations with large sets of items, it becomes less likely that any expert would be familiar with all the items, and so would be less likely to produce a correctly tailored list for all the items that need recommendations. An expert can also provide recommendations to be given for a set of items. While this is possible to consider in the case of a small number of sets, an expert will be quickly overwhelmed in any attempt to provide a comprehensive set of recommendations given the large number of possible combinations of items.
The second prior art technique in recommendations manipulates customer preference data to provide a recommendation. For example, U.S. Pat. No. 4,348,740, entitled xe2x80x9cMethod and portable apparatus for comparison of stored sets of data,xe2x80x9d provides a method for sharing preference data, and U.S. Pat. No. 4,870,579, entitled xe2x80x9cSystem and method of predicting subjective reactions,xe2x80x9d describes a method of using that shared preference data to provide recommendations. Other techniques have built upon this latter reference to promote alternative techniques of using preference data to provide recommendations. There are a number of situations in which using preference data does not generate reasonable recommendations. The first is when, for some reason, preference data is not obtainable, such as for a newly introduced item. The second is when the recommendation is for a task or a situation where preferences are not the overriding concern. For instance, no matter how well-liked a xe2x80x9cbicyclexe2x80x9d is, if the task is moving furniture, a less preferred xe2x80x9ctruckxe2x80x9d would be a more appropriate recommendation than any type of xe2x80x9cbicyclexe2x80x9d.
The second thread of pertinent background subject matter (item (2) above) is the use of relevance feedback in information retrieval tasks. Relevance feedback consists of the idea of modifying a subsequent information query by using feedback from the user as to the relevance of information retrieved in a previous query. For instance, a user enters a query, and an information retrieval system returns a set of responses. The user then indicates which of these responses is most relevant to the query, and the query is modified to use this relevance information in producing another query. The first use of relevance feedback is attributed to Rocchio in the reference xe2x80x9cDocument retrieval systemsxe2x80x94optimization and evaluationxe2x80x9d, a Doctoral Dissertation by Rocchio J. J. Jr. (1966), Harvard University, in Report ISR-10, to the National Science Foundation, Harvard computational Laboratory, Cambridge, Mass. This is the seminal document referred to in modem relevance feedback literature such as the xe2x80x9cImproving Retrieval Performance by Relevance Feedbackxe2x80x9d chapter by Salton and Buckley (1988) in xe2x80x9cReadings in Information Retrievalxe2x80x9d, edited by Jones and Willett, 1997.
The prior art is devoid of a method such that the two threads of pertinent prior art coalesced whereby relevance feedback is used to automatically provide recommendations.
Shortcomings and limitations of the prior art are obviated, in accordance with the present invention, by a methodology and concomitant circuitry wherein the customer (alternatively, purchaser, consumer, or user, as the case may be) is allowed to find items using either a search engine or by browsing a catalog of items. Then the act of placing an item in the customer""s electronic shopping basket (alternatively, shopping cart) is used as an indication that the customer prefers that item. Finally, the basket item provides relevance feedback upon which to build a query to find items like the item in the basket. Thus, in accordance with the present invention, relevance feedback serves as the basis whereby the relevance feedback is modified and then applied to supply a recommendation.
Broadly, in accordance with a method aspect of the present invention, a method for automatically recommending textual items stored in a database to a user of a computer-implemented service, the user having selected one of the items, includes: (a) applying a latent semantic algorithm to the textual items to establish a conceptual similarity among the textual items and the selected item; and (b) outputting to the user a recommended set of nearest items to the selected item based upon the conceptual similarity.
Broadly, in accordance a one system aspect of the present invention, a system for automatically recommending textual items stored in a database to a user of a computer-implemented service includes: (a) a processor for applying a latent semantic algorithm to the textual items to establish a conceptual similarity among the textual items and one of the items selected by the user; and (b) means for outputting to the user a recommended set of nearest items to the selected item with reference to the conceptual similarity among the textual items and the selected item.